R Notebook

library(VennDiagram)

## Loading required package: grid

## Loading required package: futile.logger

library(Seurat)

## Loading required package: ggplot2

## Loading required package: cowplot

## 
## Attaching package: 'cowplot'

## The following object is masked from 'package:ggplot2':
## 
##     ggsave

## Loading required package: Matrix

load('../../data/markergenes/markergenes-crossed.negbinom.supra4-subq4-removed')
markers <- df.cluster_markers

Marker genes of the intersection of Supra and Peri. Supra has 111 marker genes, Peri 54. They have 15 genes in common.

genes.peri <- markers[markers$cluster == 'Peri.white', 'gene']
genes.supra <- markers[markers$cluster == 'Supra.white', 'gene']
peri.supra <- intersect(genes.peri, genes.supra)

grid.newpage()
draw.pairwise.venn(length(genes.peri), length(genes.supra), length(peri.supra), category=c('Peri', 'Supra'), lty=rep('blank', 2), fill=c('#f67770', '#1fbfc3'), scaled=T, cat.fontfamily = rep("helvetica", 2))

## (polygon[GRID.polygon.11], polygon[GRID.polygon.12], polygon[GRID.polygon.13], polygon[GRID.polygon.14], text[GRID.text.15], text[GRID.text.16], text[GRID.text.17], text[GRID.text.18], text[GRID.text.19])

Marker genes of the intersection of Subq and Visce. Subq has 91 marker genes, Visce 11. They have only 2 genes in common.

genes.visce <- markers[markers$cluster == 'Visce.brown', 'gene']
genes.subq <- markers[markers$cluster == 'Subq.brown', 'gene']
visce.subq <- intersect(genes.visce, genes.subq)

grid.newpage()
draw.pairwise.venn(length(genes.visce), length(genes.subq), length(visce.subq), category=c('Visce', 'Subq'), lty=rep('blank', 2), fill=c('#c680fc', '#7dac1f'), scaled=T, cat.fontfamily = rep("helvetica", 2))

## (polygon[GRID.polygon.20], polygon[GRID.polygon.21], polygon[GRID.polygon.22], polygon[GRID.polygon.23], text[GRID.text.24], text[GRID.text.25], text[GRID.text.26], lines[GRID.lines.27], text[GRID.text.28], text[GRID.text.29])

all10x <- readRDS('../../data/10x')

Markers intersection Peri and Supra

print(peri.supra)

##  [1] "TM4SF1"      "CRYAB"       "PTX3"        "ACAN"        "SERTAD4-AS1"
##  [6] "MYL9"        "MGST3"       "RPSA"        "LY6K"        "MTRNR2L8"   
## [11] "NPM1"        "RPS26"       "FHL2"        "HSPA8"       "PENK"

markers[which(markers$gene %in% peri.supra),]

markers.peri.supra <- unique(markers[which(markers$gene %in% peri.supra),'gene'])

VlnPlot(all10x, features.plot=markers.peri.supra, group.by='sample_name', point.size.use=-1, nCol=2, x.lab.rot=T, size.x.use=10)

Markers intersection Subq and Visce

print(visce.subq)

## [1] "HOXB7" "GREM1"

print(markers[which(markers$gene %in% visce.subq),])

##         cluster         p_val avg_logFC pct.1 pct.2     p_val_adj  gene
## 203 Visce.brown  3.746971e-50 0.2529170 0.540 0.343  9.021956e-46 HOXB7
## 205 Visce.brown  8.453013e-26 0.3361652 0.771 0.713  2.035316e-21 GREM1
## 215  Subq.brown  0.000000e+00 0.6455935 0.922 0.713  0.000000e+00 GREM1
## 229  Subq.brown 1.312154e-255 0.2938827 0.614 0.343 3.159405e-251 HOXB7

VlnPlot(all10x, features.plot=toupper(c('GREM1', 'HOXB7')), group.by='sample_name', point.size.use=-1, nCol=2, x.lab.rot=T, size.x.use=10)

Top markers Peri

print(markers[markers$cluster == 'Peri.white',][1:20,])

##       cluster p_val avg_logFC pct.1 pct.2 p_val_adj        gene
## 1  Peri.white     0 0.8700187 0.963 0.655         0        SRGN
## 2  Peri.white     0 0.8337517 0.928 0.426         0      TM4SF1
## 3  Peri.white     0 0.6715377 0.994 0.960         0       CRYAB
## 4  Peri.white     0 0.6681469 0.830 0.431         0        MEST
## 5  Peri.white     0 0.6314312 0.564 0.183         0        NEFM
## 6  Peri.white     0 0.6023525 0.961 0.805         0        PTX3
## 7  Peri.white     0 0.5681777 0.932 0.662         0        RGS4
## 8  Peri.white     0 0.5420506 0.698 0.404         0    MTRNR2L1
## 9  Peri.white     0 0.4967890 0.999 0.981         0    HSP90AA1
## 10 Peri.white     0 0.4948082 0.967 0.813         0      GLIPR1
## 11 Peri.white     0 0.4855498 0.917 0.660         0        CTSC
## 12 Peri.white     0 0.4615908 0.866 0.670         0       KRT18
## 13 Peri.white     0 0.4529379 0.375 0.135         0       KISS1
## 14 Peri.white     0 0.4414635 0.999 0.968         0       TAGLN
## 15 Peri.white     0 0.3857197 0.592 0.198         0        ACAN
## 16 Peri.white     0 0.3764161 1.000 0.987         0       COTL1
## 17 Peri.white     0 0.3638208 0.956 0.799         0      SCUBE3
## 18 Peri.white     0 0.3607074 0.999 0.984         0       HMGA1
## 19 Peri.white     0 0.3596772 0.645 0.213         0 SERTAD4-AS1
## 20 Peri.white     0 0.3591175 0.823 0.583         0        FGF5

VlnPlot(all10x, features.plot=as.vector(markers[markers$cluster == 'Peri.white',][1:20,'gene']), group.by='sample_name', point.size.use=-1, nCol=2, x.lab.rot=T, size.x.use=10)

Top markers Supra

print(markers[markers$cluster == 'Supra.white',][1:20,])

##        cluster p_val avg_logFC pct.1 pct.2 p_val_adj      gene
## 70 Supra.white     0 2.1583210 0.730 0.146         0    IGFBP5
## 71 Supra.white     0 1.0851716 0.987 0.832         0    IGFBP3
## 72 Supra.white     0 0.7884681 0.981 0.808         0    AKAP12
## 73 Supra.white     0 0.6945874 0.774 0.433         0 TNFRSF11B
## 74 Supra.white     0 0.6845614 0.903 0.513         0     IFI27
## 75 Supra.white     0 0.6784346 0.898 0.523         0    PPAP2B
## 76 Supra.white     0 0.6674789 0.760 0.382         0    EFEMP1
## 77 Supra.white     0 0.6591687 0.994 0.877         0     MFAP5
## 78 Supra.white     0 0.6527350 0.580 0.142         0      G0S2
## 79 Supra.white     0 0.6478446 0.943 0.687         0       DCN
## 80 Supra.white     0 0.6370642 0.875 0.615         0    CYP1B1
## 81 Supra.white     0 0.5793271 0.992 0.983         0    IGFBP6
## 82 Supra.white     0 0.5775069 0.981 0.889         0    COL6A2
## 83 Supra.white     0 0.5583988 0.902 0.568         0      JUNB
## 84 Supra.white     0 0.5299842 0.799 0.485         0       FOS
## 85 Supra.white     0 0.5295200 0.954 0.799         0      CTGF
## 86 Supra.white     0 0.5223843 0.953 0.766         0     CRLF1
## 87 Supra.white     0 0.5220216 0.979 0.705         0  MTRNR2L8
## 88 Supra.white     0 0.5190278 0.617 0.223         0       LXN
## 89 Supra.white     0 0.5169242 0.973 0.933         0      FBN1

VlnPlot(all10x, features.plot=as.vector(markers[markers$cluster == 'Supra.white',][1:20,'gene']), group.by='sample_name', point.size.use=-1, nCol=2, x.lab.rot=T, size.x.use=10)

Top markers Visce

print(markers[markers$cluster == 'Visce.brown',][1:20,])

##          cluster         p_val avg_logFC pct.1 pct.2     p_val_adj
## 196  Visce.brown  0.000000e+00 0.3428612 1.000 1.000  0.000000e+00
## 197  Visce.brown 2.181113e-278 0.2504584 0.325 0.003 5.251685e-274
## 198  Visce.brown 1.552694e-261 0.4773603 1.000 1.000 3.738576e-257
## 199  Visce.brown 1.739342e-219 0.4227270 0.588 0.409 4.187987e-215
## 200  Visce.brown 4.905554e-139 0.2915473 0.546 0.396 1.181159e-134
## 201  Visce.brown 5.156757e-133 0.2963781 0.400 0.247 1.241644e-128
## 202  Visce.brown 6.905548e-102 0.2602546 0.838 0.913  1.662718e-97
## 203  Visce.brown  3.746971e-50 0.2529170 0.540 0.343  9.021956e-46
## 204  Visce.brown  8.626536e-29 0.2906204 0.595 0.465  2.077097e-24
## 205  Visce.brown  8.453013e-26 0.3361652 0.771 0.713  2.035316e-21
## 206  Visce.brown  6.221691e-11 0.4410880 0.863 0.862  1.498059e-06
## 207  Visce.brown  2.387362e-04 0.2557619 0.464 0.372  1.000000e+00
## 208  Visce.brown  3.884867e-02 0.2659491 0.635 0.587  1.000000e+00
## NA          <NA>            NA        NA    NA    NA            NA
## NA.1        <NA>            NA        NA    NA    NA            NA
## NA.2        <NA>            NA        NA    NA    NA            NA
## NA.3        <NA>            NA        NA    NA    NA            NA
## NA.4        <NA>            NA        NA    NA    NA            NA
## NA.5        <NA>            NA        NA    NA    NA            NA
## NA.6        <NA>            NA        NA    NA    NA            NA
##               gene
## 196           FTH1
## 197          BARX1
## 198            FTL
## 199            LUM
## 200          HOXA5
## 201          SFRP1
## 202           GLRX
## 203          HOXB7
## 204  RP11-173B14.5
## 205          GREM1
## 206       SERPINE2
## 207         FAM43A
## 208          THBS2
## NA            <NA>
## NA.1          <NA>
## NA.2          <NA>
## NA.3          <NA>
## NA.4          <NA>
## NA.5          <NA>
## NA.6          <NA>

VlnPlot(all10x, features.plot=as.vector(markers[markers$cluster == 'Visce.brown',][,'gene']), group.by='sample_name', point.size.use=-1, nCol=2, x.lab.rot=T, size.x.use=10)

Top markers Subq

print(markers[markers$cluster == 'Subq.brown',][1:20,])

##        cluster         p_val avg_logFC pct.1 pct.2     p_val_adj
## 209 Subq.brown  0.000000e+00 1.2831116 0.897 0.710  0.000000e+00
## 210 Subq.brown  0.000000e+00 1.0710639 1.000 1.000  0.000000e+00
## 211 Subq.brown  0.000000e+00 1.0433224 0.985 0.947  0.000000e+00
## 212 Subq.brown  0.000000e+00 0.9901528 0.509 0.093  0.000000e+00
## 213 Subq.brown  0.000000e+00 0.7693235 0.855 0.743  0.000000e+00
## 214 Subq.brown  0.000000e+00 0.6620112 0.881 0.753  0.000000e+00
## 215 Subq.brown  0.000000e+00 0.6455935 0.922 0.713  0.000000e+00
## 216 Subq.brown  0.000000e+00 0.6268227 0.565 0.431  0.000000e+00
## 217 Subq.brown  0.000000e+00 0.5003979 0.311 0.078  0.000000e+00
## 218 Subq.brown  0.000000e+00 0.3674380 0.457 0.226  0.000000e+00
## 219 Subq.brown  0.000000e+00 0.3661562 0.551 0.332  0.000000e+00
## 220 Subq.brown  0.000000e+00 0.3056729 1.000 1.000  0.000000e+00
## 221 Subq.brown  0.000000e+00 0.2870511 1.000 1.000  0.000000e+00
## 222 Subq.brown  0.000000e+00 0.2666346 1.000 1.000  0.000000e+00
## 223 Subq.brown  0.000000e+00 0.2645295 0.426 0.076  0.000000e+00
## 224 Subq.brown 3.122433e-301 0.3503707 0.713 0.701 7.518193e-297
## 225 Subq.brown 1.090240e-299 0.4407373 0.705 0.507 2.625079e-295
## 226 Subq.brown 1.441828e-288 0.3504987 1.000 0.999 3.471634e-284
## 227 Subq.brown 1.596441e-259 0.3904153 1.000 1.000 3.843911e-255
## 228 Subq.brown 6.835613e-259 0.4182473 0.637 0.459 1.645879e-254
##           gene
## 209      THBS1
## 210      RPS29
## 211      TIMP3
## 212     BCYRN1
## 213       DKK1
## 214 AC009501.4
## 215      GREM1
## 216       HES1
## 217      ACTC1
## 218      TRNP1
## 219       NMT1
## 220      RPL37
## 221      RPL26
## 222      RPL39
## 223       NRN1
## 224    FAM101B
## 225    TINAGL1
## 226      RPL38
## 227     RPL37A
## 228       AAK1

Most of the markers for Subq seem to be found because Subq_3 stands out (RPS29, BCYRN1, RPL36A, ATP5I, AC009501.4, RPS10). Mostly ribosomal genes.

BCYRN1 = brain cytoplasmic RNA

VlnPlot(all10x, features.plot=as.vector(markers[markers$cluster == 'Subq.brown',][1:20,'gene']), group.by='sample_name', point.size.use=-1, nCol=2, x.lab.rot=T, size.x.use=10)